Incorporating Commonsense Knowledge Graph in Pretrained Models for Social Commonsense Tasks

Ting-Yun Chang, Yang Liu, Karthik Gopalakrishnan, Behnam Hedayatnia, Pei Zhou, Dilek Hakkani-Tur

Abstract
Pretrained language models have excelled at many NLP tasks recently; however, their social intelligence is still unsatisfactory. To enable this, machines need to have a more general understanding of our complicated world and develop the ability to perform commonsense reasoning besides fitting the specific downstream tasks. External commonsense knowledge graphs (KGs), such as ConceptNet, provide rich information about words and their relationships. Thus, towards general commonsense learning, we propose two approaches to implicitly and explicitly infuse such KGs into pretrained language models. We demonstrate our proposed methods perform well on SocialIQA, a social commonsense reasoning task, in both limited and full training data regimes.
Anthology ID:
2020.deelio-1.9
Volume:
Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures
Month:
November
Year:
2020
Address:
Online
Venue:
DeeLIO
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
74–79
Language:
URL:
https://aclanthology.org/2020.deelio-1.9
DOI:
10.18653/v1/2020.deelio-1.9
Bibkey:
Cite (ACL):
Ting-Yun Chang, Yang Liu, Karthik Gopalakrishnan, Behnam Hedayatnia, Pei Zhou, and Dilek Hakkani-Tur. 2020. Incorporating Commonsense Knowledge Graph in Pretrained Models for Social Commonsense Tasks. In Proceedings of Deep Learning Inside Out (DeeLIO): The First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, pages 74–79, Online. Association for Computational Linguistics.
Cite (Informal):
Incorporating Commonsense Knowledge Graph in Pretrained Models for Social Commonsense Tasks (Chang et al., DeeLIO 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/nodalida-main-page/2020.deelio-1.9.pdf
Video:
 https://slideslive.com/38939732
Data
ATOMIC, ConceptNet, SIQA
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